Factors Associated With Symptom Burden Among Pediatric Patients With Cancer
Bibliographic record
Abstract
PURPOSE The objective was to identify factors associated with self-reported symptom burden measured using Symptom Screening in Pediatrics Tool (SSPedi) in pediatric patients with cancer. METHODS This was a secondary analysis of a cluster randomized trial enrolling pediatric patients newly diagnosed with cancer. Twenty sites were randomized to routine symptom screening versus usual care. Intervention included thrice-weekly symptom screening with SSPedi, delivery of severely bothersome scores to health care teams, and implementation of locally adapted symptom management care pathways. Primary outcome was total SSPedi scores, 0 (no bothersome symptoms) to 60 (worst bothersome symptoms), obtained at baseline, week four, and week eight in 430 patients (n = 217 intervention and n = 213 usual care). We created a mixed linear regression model evaluating design (including time point), patient/guardian, and site characteristics for their associations with symptom burden after controlling for treatment assignment. RESULTS SSPedi scores were significantly lower at weeks 4 and 8 compared with baseline ( P < .0001 overall), and at intervention versus control sites ( P < .0001). In the full model, males (estimate, –3.3 [95% CI, –4.6 to –2.0]; P < .0001) and sites with higher physician staffing ratios (each physician full-time equivalent per 100 new diagnoses estimate –0.20 [95% CI, –0.5 to 0.0]; P = .024) had significantly lower total SSPedi scores. CONCLUSION Total symptom burden was reduced by time, intervention (symptom screening and care pathways), and greater physician staffing ratio. Females had higher symptom burden. These data may inform programmatic implementation of routine symptom screening in pediatric patients with cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".